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A Multi-modal Data Platform for Diagnosis and Prediction of Alzheimer’s Disease Using Machine Learning Methods

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Abstract

Alzheimer’s an irreversible neurodegenerative disease with the most far-reaching impact, the most extensive, and the most difficult to cure in the world. It is also the most common disease of Alzheimer’s disease. With the rapid rise of data mining, machine learning and other fields, they have penetrated various disciplines. In particular, research in the field of AD is developing rapidly and has demonstrated strong vitality. In terms of data, Alzheimer’s Disease Neuroimaging Initiative (ADNI) researchers collect, verify and use a variety of data modalities as predictors of disease, including MRI and PET images, genetics, cognitive testing, cerebrospinal fluid and blood biomarkers, etc. Therefore, this paper uses a multi-task learning algorithm based on the ADNI data set to implement regression tasks and predict the cognitive scores of subjects in the next 3 years. This method can effectively assess the cognitive trends of patients in the future and aims to predict the progression of the disease. In addition, we used four different machine learning classification algorithms to conduct fusion research on AD multi-modal data, including MRI, PET, and cognitive scoring data. This method can determine the current patient’s cognitive stage, to achieve the effect of assisting doctors in diagnosis. Finally, we designed a multi-modal data platform technical architecture to standardize management and sharing of ADNI data and data obtained by offline medical institutions to improve the utilization and value of data. The design of the technical architecture proposed in this article is more easily scalable and compatible with other neurological diseases. Nowadays, the large amount of data being generated by AD can provide valuable solutions for the research of disease progression prediction and auxiliary diagnosis.

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References

  1. Desai AK, Grossberg GT (2005) Diagnosis and treatment of Alzheimer’s disease. Neurology 64(12 suppl 3):S34–S39

    Article  Google Scholar 

  2. Khachaturian ZS (1985) Diagnosis of Alzheimer’s disease. Arch Neurol 42(11):1097–1105

    Article  Google Scholar 

  3. Rosen WG, Mohs RC, Davis KL (1984) A new rating scale for Alzheimer’s disease. Am J Psychiatry 141(11):1356–1364

    Article  Google Scholar 

  4. Petersen RC, Smith GE, Waring SC, Ivnik RJ, Tangalos EG, Kokmen E (1999) Mild cognitive impairment: clinical characterization and outcome. Arch Neurol 56(3):303–308

    Article  Google Scholar 

  5. “2019 Alzheimer’s disease facts and figures,” Alzheimer’s & Dementia, 15: 321–387. https://doi.org/10.1016/j.jalz.2019.01.010

  6. Cummings JL, Doody R, Clark C (2007) Disease-modifying therapies for Alzheimer disease challenges to early intervention. Neurology 69(16):1622–1634

    Article  Google Scholar 

  7. Petersen RC, Aisen PS, Beckett LA, Donohue MC, Gamst AC, Harvey DJ, Jack CR, Jagust WJ, Shaw LM, Toga AW, Trojanowski JQ, Weiner MW (2010) Alzheimer’s Disease Neuroimaging Initiative (ADNI) clinical characterization. Neurology 74(3):201–209

    Article  Google Scholar 

  8. Jack CR, Petersen RC, O’Brien PC, Tangalos EG (1992) MRI-based hippocampal volumetry in the diagnosis of Alzheimer’s disease. Neurology 42(1):183–188

    Article  Google Scholar 

  9. Coleman RE (2007) Positron emission tomography diagnosis of Alzheimer’s disease. PET Clin 2(1):25–34

    Article  Google Scholar 

  10. Holtzman DM (2011) CSF biomarkers for Alzheimer’s disease: current utility and potential future use. Neurobiol Aging 32(Supplement):1

    Google Scholar 

  11. Ogescu C, Plaisanu C, Bistriceanu D (2008) “Web based platform for management of heterogeneous medical data,” 2008 IEEE International Conference on Automation, Quality and Testing, Robotics, Cluj-Napoca, pp. 257–260, 2008

  12. Lyu D, Tian Y, Wang Y, Tong D, Yin W, Li J (2015) “Design and Implementation of Clinical Data Integration and Management System Based on Hadoop Platform,” 2015 7th International Conference on Information Technology in Medicine and Education (ITME), Huangshan, pp. 76–79, 2015

  13. Lizarraga G, Cabrerizo M, Duara R, Rojas N, Adjouadi M, Loewenstein D (2016) “A Web Platform for data acquisition and analysis for Alzheimer’s disease,” Southeast Con 2016, Norfolk, pp. 1–5

  14. Pang Z, Zhang S, Yang Y, Qi J, Yang P (2020) “Interoperable Multi-Modal Data Analysis Platform for Alzheimer’s Disease Management,” 2020 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), pp. 1321–1327. https://doi.org/10.1109/ISPA-BDCloud-SocialCom-SustainCom51426.2020.00196

  15. Thung KH, Wee CY (2018) A brief review on multi-task learning. Multimed Tools Appl 77(22):29705–29725

    Article  Google Scholar 

  16. Zhou J, Yuan L, Liu J, Ye J (2011) “A multi-task learning formulation for predicting disease progression,” in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 814–822

  17. Qi J, Yang P, Newcombe L, Peng X, Yang Y, Zhao Z (2020) An overview of data fusion techniques for internet of things enabled physical activity recognition and measure. Inf Fusion 55:269–280

    Article  Google Scholar 

  18. Gong P, Ye J, Zhang C (2013) “Multi-stage multi-task feature learning,” J Mach Learn Res

  19. Argyriou A, Evgeniou T, Pontil M (2007) “Multi-task feature learning,”

  20. Argyriou A, Evgeniou T, Pontil M (2008) “Convex multi-task feature learning,” Mach Learn

  21. Zhou J, Liu J, Narayan VA, Ye J, (2012) “Modeling disease progression via fused sparse group lasso,” in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1095–1103

  22. Cao P, Shan X, Zhao D, Huang M, Zaiane O (2017) Sparse shared structure based multi-task learning for MRI based cognitive performance prediction of Alzheimer’s disease. Pattern Recognit 72:219–235

    Article  Google Scholar 

  23. Liu X, Goncalves AR, Cao P, Zhao D, Banerjee A (2018) Modeling Alzheimer’s disease cognitive scores using multi-task sparse group lasso. Comput Med Imaging Graph 66:100–114

    Article  Google Scholar 

  24. Wang M, Zhang D, Shen D, Liu M (2019) Multi-task exclusive relationship learning for alzheimer’s disease progression prediction with longitudinal data. Med Image Anal 53:111–122

    Article  Google Scholar 

  25. Ito K, Corrigan B, Zhao Q et al (2011) Disease progression model for cognitive deterioration from Alzheimer’s disease neuroimaging initiative database. Alzheimer’s Dement 7(2):151–160

    Article  Google Scholar 

  26. Stonnington CM, Chu C, Klöppel S, Jack CR, Ashburner J, Frackowiak RSJ (2010) Predicting clinical scores from magnetic resonance scans in Alzheimer’s disease. Neuroimage 51(4):1405–1413

    Article  Google Scholar 

  27. Chen T, Guestrin C (2016) “XGBoost: a scalable tree boosting system,” In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ‘16). Association for Computing Machinery, New York, pp. 785–794. https://doi.org/10.1145/2939672.2939785

  28. Nan F, Yang P, Meng Q, Xie Y, Zhang D, Muhammad K (2019) GAN-based semi-supervised learning approach for clinical decision support in health-IoT platform. IEEE Access 7:8048–8057

    Article  Google Scholar 

  29. Győrödi C, Győrödi R, Pecherle G, Olah A, (2015) “A comparative study: MongoDB vs. MySQL,”2015 13th International Conference on Engineering of Modern Electric Systems (EMES), Oradea.

  30. Taylor RH, Rose F, Toher C, Levy O, Yang K, Nardelli MB, Curtarolo S (2014) “A RESTful API for exchanging materials data in the AFLOWLIB.org consortium,” Computational Mater Sci, 93.

  31. Chen X, Fang X, Lin X (2012) “Ajax-based Positioning System for Coal Miners,” 2012 Third World Congress on Software Engineering, Wuhan.

  32. Agocs A, Goff JL (2018) “A web service based on RESTful API and JSON Schema/JSON Meta Schema to construct knowledge graphs,” 2018 International Conference on Computer, Information and Telecommunication Systems (CITS), Colmar.

  33. Wang S, Li X, Duan S, Bu Z, Jian X, He C (2019) “Modeling and Simulation of Radar Klystron Based on the System Vue,”2019 International Conference on Meteorology Observations (ICMO), Chengdu.

  34. Ying-kui D, Yang W, Ping G, Yue P, LiJuan Z, Shu L (2019) “Cloud Data Monitoring Management and Visual Application System Based on Spring Boot," 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chengdu.

  35. Guntupally K, Devarakonda R, Kehoe K (2018) “Spring boot based REST API to improve data quality report generation for big scientific data: ARM data center example,” 2018 IEEE International Conference on Big Data (Big Data), Seattle.

  36. Zhang Y, Guo Y, Yang P, Chen W, Lo B (2020) Epilepsy seizure prediction on EEG using common spatial pattern and convolutional neural network. IEEE J Biomed Health Inform 24(2):465–474

    Article  Google Scholar 

  37. Moore B, Berger T, Song D (2020) “Validation of a Convolutional Neural Network Model for Spike Transformation Using a Generalized Linear Model,” 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 3236–3239, https://doi.org/10.1109/EMBC44109.2020.9176458

  38. Fan D, Yang J, Zhang J, Lv Z, Huang H, Qi J, Yang P (2018) “Effectively measuring respiratory flow with portable pressure data using back propagation neural network,” in IEEE Journal of Translational Engineering in Health and Medicine, vol. 6, pp. 1–12, Art no. 1600112

  39. Xin R, Zhang J, Shao Y (2020) Complex network classification with convolutional neural network. Tsinghua Sci Technol 25(4):447–457. https://doi.org/10.26599/TST.2019.9010055

    Article  Google Scholar 

  40. Deng Z, Yang P, Zhao Y, Zhao X, Dong F (2015) “Life-Logging Data Aggregation Solution for Interdisciplinary Healthcare Research and Collaboration,” 2015 IEEE International Conference on Computer and Information Technology, pp. 2315–2320.

  41. Cinel G, Tarim EA, Tekin HC (2020) Wearable respiratory rate sensor technology for diagnosis of sleep apnea. Med Technol Congress (TIPTEKNO) 2020:1–4. https://doi.org/10.1109/TIPTEKNO50054.2020.9299255

    Article  Google Scholar 

  42. Mohsen S, Zekry A, Abouelatta M, Youssef K, (2020) “A self-powered wearable sensor node for IoT healthcare applications,” 2020 8th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC), pp. 70–73, https://doi.org/10.1109/JAC-ECC51597.2020.9355925

  43. Qi J, Yang P, Waraich A, Deng Z, Zhao Y, Yang Y (2018) Examining sensor-based physical activity recognition and monitoring for healthcare using internet of things: a systematic review. J Biomed Informatics. https://doi.org/10.1016/j.jbi.2018.09.002

    Article  Google Scholar 

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Acknowledgements

This research process is very grateful for the strong support of Kunming Hospital and this work was supported by the Yunnan University’s Research Innovation Fund for Graduate Students (No. 2020228).

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Authors and Affiliations

  1. National Pilot School of Software for Yunnan University, Kunming, China

    Zhen Pang, Xiang Wang, Xulong Wang & Yun Yang

  2. Department of Computer Science and Software Engineering, Xi’an JiaoTong-Liverpool University, Suzhou, China

    Jun Qi

  3. Department of Neurology, the First People’s Hospital of Yunnan Province, Kunming, China

    Zhong Zhao & Yuan Gao

  4. Department of Computer Science Faculty of Engineering, University of Sheffield, Sheffield, UK

    Po Yang

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  1. Zhen Pang
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  2. Xiang Wang
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  3. Xulong Wang
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  4. Jun Qi
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  5. Zhong Zhao
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  6. Yuan Gao
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  7. Yun Yang
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  8. Po Yang
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Correspondence to Jun Qi, Zhong Zhao or Yun Yang.

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Pang, Z., Wang, X., Wang, X. et al. A Multi-modal Data Platform for Diagnosis and Prediction of Alzheimer’s Disease Using Machine Learning Methods. Mobile Netw Appl 26, 2341–2352 (2021). https://doi.org/10.1007/s11036-021-01834-1

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